The use of medical images to guide neurosurgical procedures is ubiquitous. The brain is a complex organ, full of structures and vessels that are at once crucial to a patient's survival and well-being, and difficult to perceive unaided by imaging technology. Three-dimensional imaging techniques, and particularly the magnetic resonance imaging techniques using various contrast weights, that have become available, have made the various internal features of the brain easier to discern, paving the way for subtler, safer, and more effective procedures. However, even the current battery of imaging technology, and in particular that available in common clinical settings, is not sufficient to portray the needed information for the physician to assure the best outcomes.
The subthalamic nucleus (STN) is a small oval (lens-shaped) nucleus in the brain, located ventral to the thalamus. In terms of anatomy, it is the major part of subthalamus and is part of the basal ganglia system. Often, issues such as sensitivity limitations of the current clinical imaging methods available to physicians make it difficult to obtain images showing target areas such as the STN used as a deep brain stimulation target for treating Parkinson's disease. The STN is often used here as an example of the invention, while the invention applies to other brain structures as well, some critical for DBS and some for other brain procedures.
To enhance patient images, models known as atlases created using archived images or diagrams are matched and fitted to the patient images. However, these atlases are often themselves limited and are not patient-specific and/or adapted to the particular patient, and generally do not offer a sufficient range of image types and brains imaged to guarantee a useful match to the patient's brain and particular procedure. Atlases derived from post-mortem sampling, while possibly high resolution, are typically derived from single individual, and thereby do not reflect an accurate representation of the anatomy of the brain of the patient currently being evaluated. Atlases derived from averages of multiple brains do not portray the anatomical features of a specific patient and are therefore less accurate.
The STN within the sub-cortical region of the Basal ganglia is a crucial targeting structure for Deep brain stimulation (DBS) surgery, in particular for alleviating Parkinson's disease (PD) symptoms. Volumetric segmentation of such small and complex structure, which is elusive in clinical MRI protocols, is thereby a pre-requisite process for reliable DBS targeting of PD. While direct visualization and localization of the STN is facilitated with advanced high-field 7T MR Imaging, such high fields are not always clinically available. The STN is an example of a target of interest that needs to be well-localized and not readily available in clinical settings.
As mentioned, Deep brain stimulation (DBS) is a widely used neurosurgical intervention for the treatment of neuro-degenerative diseases such as Parkinson's disease (PD), Essential tremor, and psychiatric disorders. In particular, it has been reported that the DBS of the STN structure has important clinical efficacy for advanced PD. Accurate positioning of the electrodes into the STN is critical for a successful DBS procedure, since slight misplacement even within STN region may result in severe side effects.
Various targeting approaches have been reported for the STN localization in the DBS procedure. Some of those methods refer to anatomical information such as the inter-commissural distances and the ventricles, based on atlases, to localize the STN. However, in such indirect generic atlas-based targeting procedures, the variability in the position and size of the STN across individual subjects needs to be further analyzed in the context of large populations to evaluate the applicability of the STN currently used atlases.
Recent studies have addressed this issue by assessing the variability in the position and size of the STN based on the midcommissural point across PD patients. More reliable targeting approaches are based on the direct visualization and localization of the STN on the individual subject's MRI.
Recent advances in high magnetic field, for example 7 Tesla (T), MR imaging techniques allow to directly identify small and complex anatomical structures, thanks to the superior contrast and high resolution. Furthermore, subject specific 3D structures and their connectivity within the Basal ganglia and thalamic have been modeled, exploiting the benefits of 7T MRI, especially with the combination of multi-contrast MR images such as susceptibility-weighted image (SWI) and T2-weighted (T2W) image. Unfortunately, such high quality visualization is not always possible with standard clinical 1.5T (or 3T) MRI protocols. In addition to the localization, the accurate segmentation that provides spatial information such as location, dimension, and orientation of the DBS target structures in three dimensions is also a pre-requisite for the optimal electrode placement. This is in part due to the potential of lower therapeutically benefits or side-effects resulting from inadequate positioning of the DBS electrode in the STN (for PD) or other areas of other diseases.
Manual segmentation is both time consuming and mainly driven by anatomical subjectivity due in part to the lack of clear visualization with clinical, often low-field, MRI protocols. The automatic (or semi-automatic) segmentation of the STN structure is still challenging since it is small, complex shape with relatively unclear boundaries with its adjacent structure, although as mentioned above, superior contrast and high resolution at 7T MRI enable us to directly visualize and identify its location and shape. Other brain structures have similar characteristics of being difficult to localize in clinical settings and can be facilitated by high quality atlases.
Of numerous automated segmentation techniques, statistical shape model-based segmentations such as active shape models have shown their effectiveness in various applications of medical imaging. These approaches statistically model the variability of target structures across subjects and search for the best-fit by minimizing criteria that combine the shape model and the subject-specific information. The accuracy of these segmentations depends on the initialization and the quality of the actual input (i.e., subject) data. Moreover, morphological variations of brain structures across the population have been analyzed using statistical shape models.
Recent studies have proposed regression-based shape prediction approaches using statistical shape models, considering correlations between different shapes of structures. Sub-cortical brain structures are predicted by combining canonical correlation analysis and partial least squares regression (PLSR). Several regression models for the shape prediction are compared, considering the uncertainty on landmark points, and incorporating relevant predictors to further improve femur and tibia predictions based on their sparse observation, instead of the regression approach, has built a joint input-output distribution model based on the statistical shape model with various uncertainties for the shape prediction. Furthermore, estimation of confidence regions for the predicted target shape has been investigated. These shape prediction methods enable to estimate the target structures even on data with limited information within regions of interest.